Query generation for searchable content

ABSTRACT

Query generation for searchable content is provided. In some embodiments, query generation for searchable content includes receiving searchable content (e.g., the searchable content can include a unique identifier for the searchable content, such as a Uniform Resource Locator (URL) for a web site, and the web site can include one or more web pages); and generating a set of queries, the set of queries including one or more queries (e.g., the set of queries can include ranked queries) that are relevant to the searchable content.

CROSS REFERENCE TO OTHER APPLICATIONS

This application is a continuation of co-pending U.S. patent applicationSer. No. 14/566,516, entitled QUERY GENERATION FOR SEARCHABLE CONTENT,filed Dec. 10, 2014, which is a continuation of Ser. No. 14/167,042, nowU.S. Pat. No. 8,938,452 entitled QUERY GENERATION FOR SEARCHABLE CONTENTfiled Jan. 29, 2014, which is a continuation of U.S. patent applicationSer. No. 12/571,242, now U.S. Pat. No. 8,676,798, entitled QUERYGENERATION FOR SEARCHABLE CONTENT filed Sep. 30, 2009, all of which areincorporated herein by reference for all purposes.

BACKGROUND OF THE INVENTION

Search engines (e.g., web based search engines provided by variousvendors, including, for example, Google, Microsoft's Bing, and Yahoo)provide for searches of online information that includes searchablecontent (e.g., digitally stored electronic data), such as searchablecontent available via the World Wide Web (WWW). As input, a searchengine typically receives a search query (e.g., query input includingone or more terms, such as keywords, by a user of the search engine).The search engine performs the search for the search query and outputsresults that are typically presented in a ranked list, often referred toas search results or hits (e.g., links or Uniform Resource Locators(URLs) for one or more web pages and/or web sites). The search resultscan include web pages, images, audio, video, database results, directoryresults, information and other types of data.

Search engines typically provide paid search results (e.g., the firstthree results in the main listing and/or results often presented in aseparate listing on the right side of the output screen). For example,advertisers may pay for placement in such paid search results based onkeywords (e.g., keywords in search queries). Search engines alsotypically provide organic search results, also referred to as naturalsearch results. Organic search results are based on various algorithmsemployed by different search engines that attempt to provide relevantsearch results based on a received search query.

For improved Internet marketing, search engine optimization (SEO) hasdeveloped as a form of industry/technical consulting (often referred toas search engine optimizers) provided to web site operators (e.g.,vendors of products/services with web sites and/or e-commerce vendors ofproducts/services) for improving the volume or quality of traffic to aweb site from a search engine via organic search results (e.g., toimprove the web site's web presence as a paid service engagement orpursuant to a marketing campaign). Generally, the higher a web siteappears in the organic search results list, the more users it willreceive from the search engine. SEO can target different kinds ofsearch, including image search, local search, and industry specific,vertical search engines to improve the web site's web presence. Forexample, SEO often considers how search engines work and what peoplesearch for to recommend web site related changes to optimize a website(e.g., which primarily involves editing its content and HyperText MarkupLanguage (HTML) coding to both increase its relevance to specifickeywords and to remove barriers to the indexing activities of searchengines).

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are disclosed in the following detailed descriptionand the accompanying drawings.

FIG. 1 is a functional diagram illustrating a programmed computer systemfor executing query generation for searchable content in accordance withsome embodiments.

FIG. 2 is a block diagram illustrating network based search inaccordance with some embodiments.

FIG. 3 is a functional diagram for query generation for searchablecontent in accordance with some embodiments.

FIG. 4 is another functional diagram for query generation for searchablecontent in accordance with some embodiments.

FIG. 5 illustrates a flow diagram for query generation for searchablecontent in accordance with some embodiments.

FIG. 6 illustrates another flow diagram for query generation forsearchable content in accordance with some embodiments.

FIG. 7 illustrates another flow diagram for query generation forsearchable content in accordance with some embodiments.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

For improved Internet marketing, search engine optimization (SEO)generally relies on search engine optimizers to assist web siteoperators in improving the volume or quality of traffic to a web sitefrom a search engine via organic search results (e.g., to improve theweb site's web presence as a paid service engagement or pursuant to amarketing campaign). Generally, the higher a web site appears in theorganic search results list, the more users it will receive from thesearch engine, and typically appearing higher in the organic searchresults list is more important than the paid search results forimproving web presence. However, such SEO approaches are generally basedon industry/technical consulting engagements that must be paid for witheach engagement for the term of the engagement. Moreover, SEO approachesare generally based on proprietary, undisclosed practices used by SEOconsultants that are not automated computer implemented processes andcannot automatically adapt to, for example, new searchable content,competitive searchable content, and/or search engine algorithm changes.

What is needed is query generation for searchable content. In someembodiments, query generation for searchable content (e.g., an inversesearch engine) is provided. In some embodiments, query generation forsearchable content includes receiving searchable content (e.g., thesearchable content can include a unique identifier for the searchablecontent, such as a Uniform Resource Locator (URL) for a web site on theWorld Wide Web (WWW), and the web site can include one or more webpages); and generating a set of queries (e.g., generating queries usingquery modeling), the set of queries including one or more queries (e.g.,the set of queries can include ranked queries) that are relevant to thesearchable content. In some embodiments, the queries are ranked based ona score determined for each query of the set of ranked queries. In someembodiments, an inverse search engine for query generation forsearchable content is provided, in which the searchable content isreceived as an input, and the set of ranked queries is provided as anoutput. Moreover, unlike SEO approaches, query generation for searchablecontent provides, in some embodiments, an automated computer implementedprocess that can automatically adapt to, for example, new searchablecontent, competitive searchable content, and/or search engine algorithmchanges.

Query generation for searchable content can be used for variousapplications and purposes. For example, query generation for searchablecontent can be used for search engine optimization (SEO) to optimize aweb site or any other searchable content for organic search (e.g.,populating the set of queries, or a subset of the ranked queries, in awidget or in a dynamically generated/populated widget on a one or moreHTML pages of a web site). For example, using these techniques can solvethe problem for a web site owner in which competitor web sites arewinning certain queries for which the web site owner should be better onas their web site has the more responsive content for such queries.

As another example, query generation for searchable content can be usedfor search engine marketing (SEM) to identify and compete for keywordsidentified as keywords in the generated queries for the searchablecontent (e.g., to buy keywords for search engine marketing). Those ofordinary skill in the art will appreciate that there are many otherapplications and purposes for query generation for searchable content.

FIG. 1 is a functional diagram illustrating a programmed computer systemfor executing query generation for searchable content (e.g., a web site)in accordance with some embodiments. As shown, FIG. 1 provides afunctional diagram of a general purpose computer system programmed toperform query generation for searchable content in accordance with someembodiments. As will be apparent, other computer system architecturesand configurations can be used to perform context sensitive scriptediting for form design. Computer system 100, which includes varioussubsystems as described below, includes at least one microprocessorsubsystem (also referred to as a processor or a central processing unit(CPU)) 102. For example, processor 102 can be implemented by asingle-chip processor or by multiple processors. In some embodiments,processor 102 is a general purpose digital processor that controls theoperation of the computer system 100. Using instructions retrieved frommemory 110, the processor 102 controls the reception and manipulation ofinput data, and the output and display of data on output devices (e.g.,display 118). In some embodiments, processor 102 includes and/or is usedto provide the various computer/computer implemented functional elementsdescribed below with respect to FIGS. 2 through 4 and/orexecutes/performs the processes described below with respect to FIGS. 5through 7.

Processor 102 is coupled bi-directionally with memory 110, which caninclude a first primary storage, typically a random access memory (RAM),and a second primary storage area, typically a read-only memory (ROM).As is well known in the art, primary storage can be used as a generalstorage area and as scratch-pad memory, and can also be used to storeinput data and processed data. Primary storage can also storeprogramming instructions and data, in the form of data objects and textobjects, in addition to other data and instructions for processesoperating on processor 102. Also as well known in the art, primarystorage typically includes basic operating instructions, program code,data and objects used by the processor 102 to perform its functions(e.g., programmed instructions). For example, primary storage devices110 can include any suitable computer-readable storage media, describedbelow, depending on whether, for example, data access needs to bebi-directional or uni-directional. For example, processor 102 can alsodirectly and very rapidly retrieve and store frequently needed data in acache memory (not shown).

A removable mass storage device 112 provides additional data storagecapacity for the computer system 100, and is coupled eitherbi-directionally (read/write) or uni-directionally (read only) toprocessor 102. For example, storage 112 can also includecomputer-readable media such as magnetic tape, flash memory, PC-CARDS,portable mass storage devices, holographic storage devices, and otherstorage devices. A fixed mass storage 120 can also, for example, provideadditional data storage capacity. The most common example of massstorage 120 is a hard disk drive. Mass storage 112, 120 generally storeadditional programming instructions, data, and the like that typicallyare not in active use by the processor 102. It will be appreciated thatthe information retained within mass storage 112, 120 can beincorporated, if needed, in standard fashion as part of primary storage110 (e.g., RAM) as virtual memory.

In addition to providing processor 102 access to storage subsystems, bus114 can be used to provide access other subsystems and devices as well.As shown, these can include a display monitor 118, a network interface116, a keyboard 104, and a pointing device 106, as well as an auxiliaryinput/output device interface, a sound card, speakers, and othersubsystems as needed. For example, the pointing device 106 can be amouse, stylus, track ball, or tablet, and is useful for interacting witha graphical user interface.

The network interface 116 allows processor 102 to be coupled to anothercomputer, computer network, or telecommunications network using anetwork connection as shown. For example, through the network interface116, the processor 102 can receive information (e.g., data objects orprogram instructions), from another network, or output information toanother network in the course of performing method/process steps.Information, often represented as a sequence of instructions to beexecuted on a processor, can be received from and outputted to anothernetwork. An interface card or similar device and appropriate softwareimplemented by (e.g., executed/performed on) processor 102 can be usedto connect the computer system 100 to an external network and transferdata according to standard protocols. For example, various processembodiments disclosed herein can be executed on processor 102, or can beperformed across a network such as the Internet, intranet networks, orlocal area networks, in conjunction with a remote processor that sharesa portion of the processing. Additional mass storage devices (not shown)can also be connected to processor 102 through network interface 116.

An auxiliary I/O device interface (not shown) can be used in conjunctionwith computer system 100. The auxiliary I/O device interface can includegeneral and customized interfaces that allow the processor 102 to sendand, more typically, receive data from other devices such asmicrophones, touch-sensitive displays, transducer card readers, tapereaders, voice or handwriting recognizers, biometrics readers, cameras,portable mass storage devices, and other computers.

In addition, various embodiments disclosed herein further relate tocomputer storage products with a computer readable medium that includesprogram code for performing various computer-implemented operations. Thecomputer-readable medium is any data storage device that can store datawhich can thereafter be read by a computer system. Examples ofcomputer-readable media include, but are not limited to, all the mediamentioned above: magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks; and specially configured hardware devices such asapplication-specific integrated circuits (ASICs), programmable logicdevices (PLDs), and ROM and RAM devices. Examples of program codeinclude both machine code, as produced, for example, by a compiler, orfiles containing higher level code (e.g., script) that can be executedusing an interpreter.

The computer system shown in FIG. 1 is but an example of a computersystem suitable for use with the various embodiments disclosed herein.Other computer systems suitable for such use can include additional orfewer subsystems. In addition, bus 114 is illustrative of anyinterconnection scheme serving to link the subsystems. Other computerarchitectures having different configurations of subsystems can also beutilized.

FIG. 2 is a block diagram illustrating network based search inaccordance with some embodiments. For example, the Internet and, inparticular, the World Wide Web (WWW), includes searchable contentlocated on various computers, servers, and network appliances (e.g., webservers/appliances). Web browsers, such as web browser 220, provideclient software executed on a computer, such as client 210, foraccessing searchable content (e.g., web pages) on the WWW, such assearchable content provided by a server 240 (e.g., Nike's web site)and/or competitor searchable content provided by a server 250 (e.g.,Adidas' web site). As shown in FIG. 2, the client computer 210 submits aquery to the search engine 230 and receives search results back from thesearch engine 230 (e.g., Google's search engine, Yahoo′ search engine,Microsoft's Bing search engine, or another search engine). The searchengine 230 provides search results based on the query received from theclient 210. The search engine 230 typically crawls various searchablecontent on the network, (e.g., the WWW) including searchable content 240and 250 to identify relevant searchable content (e.g., to update anindex of available searchable content). The search engine 230 typicallyprovides natural or organic search results based on the query and, insome cases, can also provide paid search results (e.g., based on paidkeywords in the query submitted by the client 210 or based on othertechniques). As will be appreciated by those of ordinary skill in theart, various network architectures and software/computing solutions areavailable for providing network based search. In some embodiments, querygeneration for searchable content can be used, for example, by a website operator providing searchable content 240 by populating the set ofqueries, or a subset of the ranked queries, in a widget or in adynamically generated/populated widget on a one or more HTML pages ofthe web site providing the searchable content 240.

FIG. 3 is a functional diagram for query generation for searchablecontent in accordance with some embodiments. In some embodiments, querygeneration for searchable content (e.g., an inverse search engine isprovided). In some embodiments, searchable content is provided (e.g.,one or more web pages and/or other searchable content) as input,potential queries are analyzed for the searchable content using one ormore of the various techniques as described herein and/or similartechniques as will be apparent to those of ordinary skill in the art inview of the embodiments disclosed herein, and a set of queries for thesearchable content is provided as output. In some embodiments, theoutput set of queries include ranked queries for the searchable content.

As shown in FIG. 3, potential queries 320 are provided as input to thedomain clustering 315. For example, potential queries 320 provides a setof top queries (e.g., the top 10, 20, 100, or 1000, etc., queries) ortop weighted/scored queries (e.g., based on a threshold score/weightcriteria). In some embodiments, potential queries 320 provides thepotential queries by querying logs of one or more search engines (e.g.,web logs of one or more search engines) to identify potential webqueries. In some embodiments, potential queries 320 provide thepotential queries based on the anchors of the searchable content, basedon popular interesting phrases (e.g., using term frequency-inversedocument frequency (tf-idf) techniques and/or other techniques). Forexample, terms in the phrase can be analyzed based on popularitymodified by interestingness of the terms in the phrase (e.g., rarity ofthe terms in documents, popularity of the terms in queries and anchors,and/or pair wise interdependence of the terms based on an associationrule). In some embodiments, suggest data techniques are used to providepotential queries (e.g., using the Google search engine's suggest outputfor certain terms). In some embodiments, a query language model is usedto provide potential queries. There are various approaches to usingquery language model techniques and/or other techniques to determinepotential queries as will be appreciated by those of ordinary skill inthe art. In some embodiments, domain clustering 315 assigns a domain tothe potential queries, which can then be used to determine a set ofcandidate queries that are a subset of the potential queries based onthe domain determined for the searchable content, as further describedbelow.

As shown in FIG. 3, domain clustering 315 also receives searchablecontent 310 as input. Domain clustering 315 determines a domain for thesearchable content. For example, Nike can be assigned to a sportsshopping domain, and Nordstrom can be assigned to a clothes shoppingdomain. In some embodiments, domain clustering 315 clusters domains ofsearchable content. For example, domain clustering can be performedbased on a concurrence of web domains using association rules, hashingalgorithms, and/or other techniques. In some embodiments, fastalgorithms for mining association rules are used as will be appreciatedby those of ordinary skill in the art. In some embodiments, clusteringis performed based on web site content (e.g., using a hashing algorithm,such as minhash or other algorithms). In some embodiments, a history ofweb sites visited by user(s) can be used for clustering (e.g., based ona toolbar, ISP, and/or cookie for monitoring and tracking visited websites).

As shown in FIG. 3, candidate queries 330 are provided as input tocompetitors 325, and candidate queries 330 also receives input fromcompetitors 325. In some embodiments, candidate queries 330 candetermine how the searchable content ranks relative to its competitors(e.g., competitors to the provider of the searchable content for thatdomain, such as Nike's web site's ranking for organic search relative toAdidas). For example, determining whether the searchable content (e.g.,web site) includes an anchor and title that match can be used to analyzethe searchable content. In some embodiments, the queries for which thesearchable content performs well relative to competitor searchablecontent can be filtered and/or disregarded for various applications(e.g., SEO applications). In some embodiments, candidate queries aregenerated using a query language model/query model. In some embodiments,an N-gram model is used to model the query language. For example, usingquery model techniques can determine that “digital camera” is a goodquery, but “is digital camera” is not a good query. In particular, givena candidate phrase ph, P(ph/Q-model) provides a computation of thecandidate phrase ph, where Q-model is the N-gram model (e.g., the N-gramfrequency counts). In some embodiments, if this probability is above acertain threshold, then the phrase ph is determined to be a potentialquery.

As shown in FIG. 3, candidate queries 330 are also provided as input torelevance 335. In some embodiments, relevance 335 provides a matchingweb page (e.g., a best matching static, non-dynamically generated webpage on the web site). In some embodiments, this process is performedperiodically (e.g., as web pages age, as content is modified,added/deleted over time). In some embodiments, the search result pagefrom the web site is obtained, and then evaluated, for example, based onsearch relevance (e.g., for SEO purposes, determined if search engineswould rank this web page highly for a given query). Various techniquescan be utilized for determining a search relevance score. For example,for a given web page, let Score(P, Q), that is a function S(P,Q) is thesearch relevance score for the query Q for the page P. In someembodiments, S(P,Q) can be computed using the following function(s):S(P,Q)=Function(T(P,Q), B(P,Q), H(P,Q), L(P,Q)), in which this functionprovides a weighted linear combination (e.g., evolving weightedassignments based on search engine changes/evolution(s)), where T(P,Q)is the Title(T) score for web page P and query Q (e.g., based on howmany terms of the query are present in the Title of the web page and/orany other criteria/techniques), where B(P,Q) is the Body(B) score forweb page P and query Q (e.g., based on how many terms of the query arepresent in the Body and/or any other criteria/techniques), where H(P,Q)is the Heading(H) tag score for web page P and query Q (e.g., based onhow many terms of the query are in the Heading and/or any othercriteria/techniques), and where L(P,Q) is the Linktext(L) score for webpage P and query Q (e.g., based on how many terms of the query arepresent in the Linktext and/or any other criteria/techniques).

In some embodiments, each of these functions are computed by taking intoaccount the overlap between the content of the query and the content ofthe corresponding tag on the page. In some embodiments, F is then afunction that is used to combine these scores to provide a computedoverall score. In some embodiments, F is a weighted linear combinationfunction (e.g., each of these scores is assigned a weight and linearlycombined/added to provide a computed score/value that accounts for eachof these scores attributing to the computed score/value).

In some embodiments, if the computed score exceeds a certain threshold(e.g., a threshold value), then the page is determined to be relevantfor search engines for the candidate query. Otherwise (e.g., thecomputed score does not exceed the threshold), the page is notconsidered relevant for search engines for the candidate query. In someembodiments, the threshold is computed by analyzing the scores ofresults retrieved by the search engine for the same candidate query.

In some embodiments, content relevance (e.g., for user purposes, doesthis page have the relevant content) for a candidate query, get thesearch result page from the merchant's site. Given that the incomingpage itself is a search result page, each result is evaluated andpresented on the page to determine if this is a relevant query for thispage or not. For example, this technique can be used when merchants tendto provide results even if there is not a relevant match to therequested query. In some embodiments, a filtered list of queries is thenprovided using one or more of these techniques.

As shown in FIG. 3, relevance 335 is provided as input to topicality340. In some embodiments, topicality is applicable to SEM and/or SEOapplications/purposes. For example, the query including the term “trees”can be relevant to OSH (Orchard Supply Hardware) and Michael's ArtSupply, but trees can be deemed to be more topical to OSH in the Summerand more topical to Michael's Art Supply in the Winter (e.g., Holidaytree supplies). In some embodiments, various techniques are used todetermine the topicality, such as popular meaning. For example, thepopular meaning of a query can be determined by using a technique to getcontent for the query using a search engine (e.g., Google search, Yahoosearch, Microsoft's Bing search, or any search engine underconsideration). For example, a search for Apple may suggest computer asApple computer is a popular meaning/popular search query. In someembodiments, topicality analysis includes determining a local meaning.For example, determining the local meaning can include determining themost relevant page from the web site for the query (e.g., Apple tree forOSH) for a query including the term “Apple”. In some embodiments,topicality analysis further includes determining how different the localmeaning is from the global or popular meaning. If the local meaning andthe global or popular meaning are very different, then topicality isdetermined to be low (e.g., poor). In some embodiments, topicalityincludes determining the most clicked page for the query. In someembodiments, topicality includes analyzing the web site/searchablecontent for one or more competitors, and determining how many areadvertising or showing information in search results for this query.

As shown in FIG. 3, relevance 335 is provided as input tocompetitiveness 345. In some embodiments, competitiveness 345 determinesa link or rank, such as a page rank (e.g., an actual or proximate rangeof page rank) and/or a domain rank. For example, competitiveness 345 candetermine a number of back links and/or quality, quality of back links(e.g., based on their relative rank, as discussed above). In someembodiments, competitiveness 345 also determines a relevance, such asthe average relevance of, for example, the top ten/ranked results, suchas using a function g(y).

In some embodiments, relevance 335 is based on a link structure. In someembodiments, relevance 335 is based on the content of the web page. Insome embodiments, relevance 335 is based on a combination of a linkstructure and the content of the web page. In some embodiments, a pagerank of other pages that are present in search results is computed,which can then be used to determine if the page (for a query) will beless or more competitive. In some embodiments, a topic relevance of thepage is computed and the computed topic relevance is compared to thetopic relevance of other pages that, for example, appear in a top tier(e.g., top 10) search results in a search engine (e.g., Google, Yahoo,Microsoft Bing, or other search engines). These are various techniques,which can be performed individually or in various combinations, that canbe used to determine how competitive this web page is compared to otherpages for this query.

As shown in FIG. 3, relevance 335 is also provided as input to value345. In some embodiments, value 345 determines a query volume for thecandidate query. In some embodiments, value 345 determines a cost perclick (CPC) value for the candidate query. In some embodiments, value345 determines a conversion rate for the candidate query. In someembodiments, value 345 determines an anti-value (e.g., negativereferences, etc.) for the candidate query.

As shown in FIG. 3, topicality 340, competiveness 345, and value 350 areprovided as input to the queries function 360. In some embodiments,other functions are provided as input to the queries function 360 usingvarious techniques. In some embodiments, queries function 360 providesthe queries generated for the searchable content, in which, in someembodiments, the queries output are a subset of the candidate queriesand can also be ranked, using some or all of the above describedtechniques or using other similar techniques as will be apparent to oneof ordinary skill in the art.

FIG. 4 is another functional diagram for query generation for searchablecontent in accordance with some embodiments. FIG. 4 is similar to FIG. 3except functional diagram 400 for providing query generation forsearchable content includes a feedback function 365. As shown in FIG. 4,feedback 365 provides a feedback loop to the query generation forsearchable content 400. In some embodiments, whether the page is showingup in the results query and/or whether click through/conversion ratesare improved are types of feedback mechanisms that can be provided byfeedback function 365. For example, a click thru from a Google searchshows a referral page based on the Google search query, in whichefficacy, clicks and quality of clicks (e.g., conversion and/or time onsite determined, for example, by using a probe sending periodic signalsvia a JavaScript or other mechanism). Those of ordinary skill in the artwill appreciate that various other feedback mechanisms can be providedfor query generation for searchable content 400.

FIG. 5 illustrates a flow diagram for query generation for searchablecontent in accordance with some embodiments. At 502, searchable content(e.g., one or more web pages and/or other searchable content) isreceived. At 504, candidate queries for the searchable content areanalyzed. At 506, a set of queries is generated, in which the set ofqueries includes one or more queries that are relevant to the searchablecontent. In some embodiments, the set of queries includes one or morequeries that are ranked.

FIG. 6 illustrates another flow diagram 600 for query generation forsearchable content in accordance with some embodiments. At 602,searchable content is received. At 604, domain clustering is determined.At 606, competitors for the searchable content are determined. At 608,candidate queries for the searchable content are determined. In someembodiments, the candidate queries are a subset of received potentialqueries that are subset based on a determined domain clustering for thesearchable content, as similarly described above. At 610, relevance ofthe candidate queries for the searchable content is determined. At 612,topicality of the candidate queries for the searchable content isdetermined. At 614, a value is determined for each of the candidatequeries for the searchable content. At 616, a set of queries isgenerated, in which the set of queries includes one or more queries thatare relevant to the searchable content. In some embodiments, the set ofqueries includes one or more queries that are ranked.

FIG. 7 illustrates another flow diagram 700 for query generation forsearchable content in accordance with some embodiments. FIG. 7 issimilar to FIG. 6 except that flow diagram 700 includes a feedback loop718. In particular, at 702, searchable content is received. At 704,domain clustering is determined for the searchable content. At 706,competitors for the searchable content are determined. At 708, candidatequeries for the searchable content are determined. In some embodiments,the candidate queries are a subset of received potential queries thatare subset based on a determined domain clustering for the searchablecontent, as similarly described above. At 710, relevance of thecandidate queries for the searchable content is determined. At 712,topicality of the candidate queries for the searchable content isdetermined. At 714, a value is determined for each of the candidatequeries for the searchable content is determined. At 716, a set ofqueries is generated, in which the set of queries includes one or morequeries that are relevant to the searchable content. In someembodiments, the set of queries includes one or more queries that areranked. At 718, a feedback loop is performed for query generation forsearchable content. For example, a feedback loop can be performed assimilarly described herein with respect to FIG. 4.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. (canceled)
 2. A method, comprising: receiving anidentifier for a web site comprising searchable content, wherein thesearchable content includes one or more web pages of the web site, andwherein the web site is to be modified for higher rankings in organicsearch results of a search engine; generating a set of queries for theweb site using an inverse search engine executed on a processor, whereinthe generated set of queries includes one or more queries that arerelevant to the searchable content of the web site, wherein generatingthe set of queries comprises: determining candidate queries for thesearchable content of the web site; and determining a topicality foreach of the candidate queries; and modifying the web site for organicsearches using the generated set of queries to generate a modifiedversion of the web site.
 3. The method recited in claim 2, wherein theidentifier for the web site includes a Uniform Resource Locator (URL)for the web site.
 4. The method recited in claim 2, wherein thesearchable content of the web site is received as an input to theinverse search engine, the generated set of queries includes rankedqueries, and the generated set of queries including the ranked queriesis provided as an output from the inverse search engine.
 5. The methodrecited in claim 2, further comprising: performing a feedback loop todetermine whether the modified version of the web site is resulting inhigher rankings in organic search results of the search engine.
 6. Themethod recited in claim 2, wherein determining of the topicality foreach of the candidate queries comprises: determining a local meaning ofeach of the candidate queries; determining a global meaning or popularmeaning of each of the candidate queries; and determining a differencebetween the local meaning and the global meaning or popular meaning ofeach of the candidate queries.
 7. The method recited in claim 2, whereingenerating the set of queries further comprises: determining a relevancefor each of the candidate queries, wherein determining the relevance isbased on a number of terms of each candidate query present in a web pageof the web site.
 8. The method recited in claim 2, further comprising:generating the set of queries using query modeling.
 9. The methodrecited in claim 2, further comprising: determining competitors for thesearchable content of the web site.
 10. The method recited in claim 2,further comprising: determining potential queries for the searchablecontent of the web site; and performing a domain clustering for thesearchable content of the web site, wherein the domain clusteringcomprises classifying the web site into a domain based on web sitecontent, and wherein the candidate queries are a subset of the potentialqueries based on the domain clustering for the searchable content of theweb site.
 11. The method recited in claim 2, further comprising:determining a score for each of the candidate queries, wherein the scoreis used for ranking each of the candidate queries.
 12. The methodrecited in claim 2, further comprising: determining potential queriesfor the searchable content of the web site, wherein determiningpotential queries comprises querying a search engine log to identify thepotential queries relevant to the web site comprising the searchablecontent; performing a domain clustering for the searchable content ofthe web site, wherein the domain clustering comprises classifying theweb site into a domain based on web site content, and wherein thecandidate queries are a subset of the potential queries based on thedomain clustering for the searchable content; and determining a scorefor each of the candidate queries, wherein the score is used for rankingeach of the candidate queries.
 13. The method recited in claim 2,wherein modifying the web site further comprises: populating one or morequeries of the set of queries in a widget on a web page of the web site,wherein the web page includes at least a subset of the searchablecontent of the web site, and wherein the one or more queries of the setof queries in the widget on the web page are searchable content of theweb page.
 14. A system, comprising: a processor configured to: receivean identifier for a web site comprising searchable content, wherein thesearchable content includes one or more web pages of the web site, andwherein the web site is to be modified for higher rankings in organicsearch results of a search engine; generate a set of queries for the website using an inverse search engine, wherein the generated set ofqueries includes one or more queries that are relevant to the searchablecontent of the web site, wherein generating the set of queriescomprises: determine candidate queries for the searchable content of theweb site; and determine a topicality for each of the candidate queries;and modify the web site for organic searches using the generated set ofqueries to generate a modified version of the web site; and a memorycoupled to the processor and configured to provide the processor withinstructions.
 15. The system recited in claim 14, wherein the identifierfor the web site includes a Uniform Resource Locator (URL) for the website.
 16. The system recited in claim 14, wherein the searchable contentof the web site is received as an input to the inverse search engine,the generated set of queries includes ranked queries, and the generatedset of queries including the ranked queries is provided as an outputfrom the inverse search engine.
 17. The system recited in claim 14,wherein the processor is further configured to: performing a feedbackloop to determine whether the modified version of the web site isresulting in higher rankings in organic search results of the searchengine.
 18. The system recited in claim 14, wherein determining of thetopicality for each of the candidate queries comprises: determine alocal meaning of each of the candidate queries; determine a globalmeaning or popular meaning of each of the candidate queries; anddetermine a difference between the local meaning and the global meaningor popular meaning of each of the candidate queries.
 19. The systemrecited in claim 14, wherein generating the set of queries furthercomprises: determine a relevance for each of the candidate queries,wherein determining the relevance is based on a number of terms of eachcandidate query present in a web page of the web site.
 20. The systemrecited in claim 14, wherein the processor is further configured to:generate the set of queries using query modeling.
 21. The system recitedin claim 14, wherein the processor is further configured to: determinecompetitors for the searchable content of the web site.
 22. The systemrecited in claim 14, wherein the processor is further configured to:determine potential queries for the searchable content of the web site;and perform a domain clustering for the searchable content of the website, wherein the domain clustering comprises classifying the web siteinto a domain based on web site content, and wherein the candidatequeries are a subset of the potential queries based on the domainclustering for the searchable content of the web site.
 23. The systemrecited in claim 14, wherein the processor is further configured to:determine a score for each of the candidate queries, wherein the scoreis used for ranking each of the candidate queries.
 24. The systemrecited in claim 14, wherein the processor is further configured to:determine potential queries for the searchable content of the web site,wherein determining potential queries comprises querying a search enginelog to identify the potential queries relevant to the web sitecomprising the searchable content; perform a domain clustering for thesearchable content of the web site, wherein the domain clusteringcomprises classifying the web site into a domain based on web sitecontent, and wherein the candidate queries are a subset of the potentialqueries based on the domain clustering for the searchable content; anddetermine a score for each of the candidate queries, wherein the scoreis used for ranking each of the candidate queries.
 25. The systemrecited in claim 14, wherein modifying the web site further comprises:populating one or more queries of the set of queries in a widget on aweb page of the web site, wherein the web page includes at least asubset of the searchable content of the web site, and wherein the one ormore queries of the set of queries in the widget on the web page aresearchable content of the web page.
 26. A computer program product, thecomputer program product being embodied in a tangible, non-transitorycomputer readable storage medium and comprising computer instructionsfor: receiving an identifier for a web site comprising searchablecontent, wherein the searchable content includes one or more web pagesof the web site, and wherein the web site is to be modified for higherrankings in organic search results of a search engine; generating a setof queries for the web site using an inverse search engine, wherein thegenerated set of queries includes one or more queries that are relevantto the searchable content of the web site, wherein generating the set ofqueries comprises: determining candidate queries for the searchablecontent of the web site; and determining a topicality for each of thecandidate queries; and modifying the web site for organic searches usingthe generated set of queries to generate a modified version of the website.
 27. The computer program product recited in claim 26, wherein theidentifier for the web site includes a Uniform Resource Locator (URL)for the web site.
 28. The computer program product recited in claim 26,wherein the searchable content of the web site is received as an inputto the inverse search engine, the generated set of queries includesranked queries, and the generated set of queries including the rankedqueries is provided as an output from the inverse search engine.
 29. Thecomputer program product recited in claim 26, wherein modifying the website further comprises: populating one or more queries of the set ofqueries in a widget on a web page of the web site, wherein the web pageincludes at least a subset of the searchable content of the web site,and wherein the one or more queries of the set of queries in the widgeton the web page are searchable content of the web page.